问题描述
我正在尝试设置一个独立的 Spark 2.0 服务器来并行处理分析功能.为此,我希望有一个具有多个执行程序的工人.
我正在使用:
- 独立的 Spark 2.0
- 8 核
- 24gig 内存
- Windows 服务器 2008
- pyspark(虽然这看起来无关)
这仅用于概念验证,但我希望有 8 个执行程序,每个内核一个.
我已尝试关注有关此主题的其他主题,但由于某种原因它对我不起作用.IE:
我相信你混淆了本地和独立模式:
- 本地模式是一种开发工具,其中所有进程都在单个 JVM 中执行.通过将 master 设置为
local
、local[*]
或local[n]
,应用程序以本地模式启动.spark.executor.cores
和spark.executor.cores
在本地模式下不适用,因为只有一个嵌入式执行器. 独立模式需要独立的 Spark集群.它需要一个主节点(可以使用
SPARK_HOME/sbin/start-master.sh
脚本启动)和至少一个工作节点(可以使用SPARK_HOME/sbin/start-slave 启动.sh
脚本).SparkConf
应该使用主节点地址来创建(spark://host:port
).
I'm trying to setup a standalone Spark 2.0 server to process an analytics function in parallel. To do this I want to have a single worker with multiple executors.
I'm using :
- Standalone Spark 2.0
- 8 Cores
- 24gig RAM
- windows server 2008
- pyspark (although this appears unrelated)
This is just for pure proof of concept purposes but I want to have 8 executors, one per each core.
I've tried to follow the other threads on this topic but for some reason it's not working for me. IE:Spark Standalone Number Executors/Cores Control
My configuration is as follows:
confspark-defaults.conf
spark.cores.max = 8
spark.executor.cores = 1
I have tried to also change my spark-env.sh file to no avail. Instead what is happening is that it shows that my 1 worker only has 1 executor on it. As you can see below, it still shows the standalone with 1 executor with 8 cores to it.
I believe you mixed up local and standalone modes:
- Local mode is a development tool where all processes are executed inside a single JVM. Application is started in a local mode by setting master to
local
,local[*]
orlocal[n]
.spark.executor.cores
andspark.executor.cores
are not applicable in the local mode because there is only one embedded executor. Standalone mode requires a standalone Spark cluster. It requires a master node (can be started using
SPARK_HOME/sbin/start-master.sh
script) and at least one worker node (can be started usingSPARK_HOME/sbin/start-slave.sh
script).SparkConf
should use master node address to create (spark://host:port
).
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